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Category Archives: Analytics

Yesterday I published the 2015 update to the Top Marketing Agencies in Atlanta. It had been a year since the previous update and a lot has changed over the past 12 months! Below is the most current view into network and independent agencies in our market.

Changes include:

WPP Network

Removed Grey (Atlanta office previously closed)

Maxx Marketing (Atlanta office previously closed)

Publicis Network

Removed Nurun (Atlanta office absorbed by Razorfish)

Added Sapient (acquired by Publicis)

Omnicom Network

Removed Javelin

Added Organic Inc. (opened office in Atlanta)

IPG Network

Removed Octagon (Atlanta office previously closed)

Removed Shopper Sciences (Atlanta office previously closed)

Removed UM (Atlanta office previously closed)

Independent Agencies

Added Chemistry (formerly TG Madison; acquisition)

Removed FuseIdeas (closed office)

Removed Never Without (disqualified)

Removed Sizmek (agency pivoted, became a products company)

Added Swarm Agency

Removed Titan (closed office)

Since I regularly get asked why an agency is not on the list/how an agency get appear on the list:

A) Criteria:Member agencies of the major networks (e.g.: WPP, Omnicom, Publicis, IPG, Dentsu, MDC Partners) are automatically included based on publicly available information. Independent agencies are added based on the following qualifications:

Number of employees (15+)

Annual revenue ($3M+)

Prior history of digital marketing campaigns

Quarterly updates regarding new clients/engagements

NOTE: The slide represents a consolidated list of the top agencies. Additional criteria may/will be used reach this objective.

B) Corrections/Updates:
If find an error or would like to be considered, please contact me at my work email address (ttishgarten at arke dot com). You may also want to subscribe to my Agency Digest email list to receive noteworthy news about Atlanta agencies and updates regarding this slide. NOTE: This is a low volume list:

If you have any other feedback, please leave it below in the comments. I hope that you find this slide to be as valuable as I have.

Last week I visited with fellow technologist and Big Data evangelist Flavio Villanustre at LexisNexis. During the visit we discussed advances in data-driven marketing, a topic that I’ll be covering with a panel of experts at the upcoming OMMA Atlanta conference.

Here are some of the big takeways from our conversation:

The biggest challenge that marketers face today is having access to data. The more data one has, the better data models one can build. And better data models drive better predications. Marketers must take every opportunity that they are given to collect and share data.

The role of unstructured data (eg; photos, videos, audio) in data analysis will increase over time. For example, Google announced in 2012 that researchers used 1,000 computers to find cats in pictures. The impressive thing about this finding is the ability of computers to identify a particular object with accuracy without human intervention. This level of machine learning demonstrates that computer-enabled data analysis is something that we can take advantage of in the not-so-distant future!

We are recognizing the value of predictive analytics. While descriptive analytics, or the collection of basic metrics (eg: visitors, page views, leads, likes, +1’s, pins, etc.), is important to understand what’s happened in the past, companies want to leverage data to predict the future and drive more revenue/increase profit.

A very small number of companies worldwide (only 3%; according to Gartner Research) are beginning to use prescriptive analytics. Prescriptive analytics is a complex type of predictive analytics that allows one to test out multiple marketing models. It provides an optimal solution given a set of objectives, requirements and constraints. This is where say a company can test the impact of various promotions/discounts and shipping rates on a customer’s purchasing behavior.

Marketers have to accept the successes (and failures) of data-driven decisioning. We hate to turn decisions over to a computer because we believe that we’re smarter — we’re human after all! Unfortunately, computers typically beat our “gut feel” — our intuition is just inherently faulty (according to HBR). Marketers need to accept that we’re biased and that we must adopt a “test and optimize” processes where the answer from one set of marketing experiments inform the next set of experiments.

It is easy to see how these advances in marketing — whether it is through the introduction of marketing technology platforms, data collection practices or data analysis processes. While it is tough to predict the future (thanks Yogi Berra), I believe that marketers that ignore data-driven decisioning are poised to lose (in the long run).

Late last week, Amazon made headlines (again) for receiving a patent on a method that predicts the eventual shipping destination of a product (see coverage on TechCrunch). The proposed method enables Amazon to leverage various data points to begin shipping an item before it is actually ordered — something that is both creepy and cool! In a way, it isn’t any different than the big data solutions that the NYPD is actively using to fight off crime or what top national universities use to recruit students.

While it may seem somewhat futuristic, it actually isn’t much different than a grocer watching the weather and ordering extra beer in anticipation of a run before a snowstorm. For Amazon, it is also the natural extension of their Subscribe & Save Program which gives customers a 15% discount on regularly purchased products, such as toilet paper, cereal, and dog treats. As this program enters its third year, Amazon is poised to not only see an incremental improvement in operating costs through real-time ordering/inventory management but also to prevent others from taking this same path.

As some retailers may be concerned that their competitive advantage is slipping while data-driven organizations like Amazon are gaining marketshare, the reality of the situation is that retailers aren’t that far behind. They simply need to initiate a “test and learn” methodology where there’s a focus on measuring an outcome through data collection and analysis. As part of a holistic data strategy, retailers can collect and stitch together information from several good data sources. For example, retails can understand: